چکیده انگلیسی

In this paper, a feedforward–feedback control structure is proposed for precision motion control of a permanent magnet linear motor (PMLM) for applications which are inherently repetitive in terms of the motion trajectories. The control scheme utilises an efficient marriage of conventional PID feedback control and an intelligent feedforward control using an iterative learning control (ILC) algorithm. The PID feedback control stabilizes the PMLM system, while the ILC feedforward control enhances the trajectories tracking performance by capitalising on the experience gained from the repeated execution of the same operations. A relay automatic tuning method is developed and incorporated, so that an initial set of control settings may be automatically derived from a few cycles of self-induced controlled oscillations. This self-tuning feature enables the PMLM application system to be operated quickly near optimal conditions simply at a push-button efficiency. Extensive experimental results are presented to demonstrate the appeal and effectiveness of the proposed scheme.

مقدمه انگلیسی

Permanent magnet linear motors (PMLM) are beginning to find widespread industrial applications, particularly in those requiring a high precision in positioning resolution such as stages for various key semiconductor fabrication and inspection processes as in step and repeat micro-lithography, wafer dicing, probing and scanning probe microscopy (SPM). The main benefits of a PMLM are the high force density achievable, low thermal losses and probably most importantly, the high positioning precision and accuracy associated with the mechanical simplicity of such systems. Unlike rotary machines, linear motors require no indirect coupling mechanisms as in gear boxes, chains, and screw coupling. This greatly reduces the effects of contact-types of nonlinearities and disturbances such as backlash and frictional forces [5].
The more predominant nonlinear effects underlying a linear motor system are the various friction components (Coulomb, viscous and stiction) and force ripples (detent and reluctance forces) arising from imperfections in the underlying components [12]. PID controllers, typically used in the process industry, found their successful applications in industrial robots with quite accurate robotics modelling. In PMLM motion systems, it is hard to get an accurate model for the nonlinear effects, specifically like the cogging effect during linear motion [12]. Therefore, due to the ultra-precision positioning requirements and the low offset tolerance of their applications, the control of these systems is particularly challenging since in these application domains a conventional PID controller alone do not usually suffice. Some efforts have been made towards more advanced control of PMLM motion systems. In Otten et al. [12] a neural-network (NN) based feedforward assisted PID controller was proposed. A hybrid control strategy using a variable structure control (VSC) is suggested for submicron positioning control [7]. In these cases and more, the control framework can be described under a feedback–feedforward configuration.
In this paper, we are mainly concerned with the applications of the PMLM in areas involving repeated iterations of motion trajectories, such as pick and place assembly operations and many step and repeat positioning systems. In these typical tasks of PMLM, the time duration for the execution of an operational cycle is finite and finite-time tracking control is always difficult with conventional controllers like the PID controllers which are more suitable for set-point regulation. To achieve a better tracking performance, a feedforward controller is usually applied. In this paper, a new feedforward controller — ILC is proposed and developed as a learning enhancement to a PID feedback controller. The main objective of this feedforward term is to reject exogenous disturbances, and to compensate for the nonlinearities mentioned above which would otherwise limit the accuracy achievable with simple feedback control systems. ILC [2], exploits the repetitive nature of the tasks as experience gained to compensate for the poor or incomplete knowledge of the plant model and the disturbances present. A recent comprehensive survey of ILC can be found in Moore [11] and Xu and Bien [16]. ILC is essentially a memory-based scheme which needs to store the tracking errors and control effects of previous repetition in order to construct the control efforts of the present cycle. Thus, a discrete-time implementation is necessary. There are two common updating laws for the ILC, a P-type updating law which only considers the tracking errors as input for learning and a D-type scheme which needs to differentiate the tracking errors [2]. For practical applications, the P-type updating law has proven to be more robust and effective in implementation. An error convergence condition has been developed in Dou [8] and this is able to provide a simple guideline in the choice and design of the learning gain.
Under this proposed control structure, two sets of control gains are to be tuned before the control system may be commissioned. Tuning and retuning of these parameters using a trial and error approach is both a tedious and difficult task. In recent years, there has been substantial interest in auto-tuning for the PID controller. In the area of process control, controller automatic tuning has already developed to the stage that it is now being incorporated as a standard feature in industrial controllers. Of the various approaches possible, the relay automatic tuning technique is probably the most appealing due to its simplicity and its field-proven capability in many applications. Many PID controller auto-tuning techniques, based on relay feedback, have been proposed for process control applications [4] and [9]. Relay auto-tuning of PID controllers have also recently been developed for servo systems where an additional time delay has to be introduced into the relay so that a sustained oscillation can be excited [13] and [14]. As shown in the experimental studies of Tan and Lee [13], an effective set of PID control parameters can be very efficiently and quickly extracted from such an experiment, leading to satisfactory control performance.
The contributions of this paper are thus two-fold. First, it presents a new control configuration, based on a marriage of a conventional PID feedback control and an intelligent ILC feedforward control. Secondly, a relay auto-tuning function is incorporated to enable effective control settings to be automatically extracted from a simple closed-loop experiment, at the convenience of a push-button. Extensive experimental results are included to illustrate the effectiveness and appeal of the proposed intelligent control system for PMLM motion control. Practical issues to be considered in the actual implementation as in the choice of time delay, initialization problems in ILC, as well as measurements filtering techniques are also addressed.
The paper is organized as follows. In Section 2, a physical model of the PMLM is derived and explained. In Section 3, details of the ILC scheme are presented. Section 4 presents the detailed procedures of the PID relay auto-tuning with tuning formula given. Before presenting experimental results, simulation results are presented in Section 5 to illustrate the effectiveness of ILC+PID schemes used in this paper. In Section 6, extensive experimental results based on the laboratory testbed system are presented to demonstrate the effectiveness of the proposed ILC-enhanced motion control method with PID relay tuning. Moreover, some practical considerations like the amount of artificial time delay, initialization problems in ILC as well as the filtering techniques, etc. are briefly discussed. Finally, Section 7 concludes this paper.

نتیجه گیری انگلیسی

A new control structure has been proposed and developed for precision motion control of PMLM. It comprises of a PID feedback control and an intelligent learning feedforward control to enhance the performance achieved from feedback control alone. Relay auto-tuning of these controllers is further incorporated to enhance the application of the method. Extensive simulation and experimental results demonstrated the appeal and effectiveness of the proposed control system.